\n\u201cWe equip people with these sensors, we measure when and where they are stressed, it comes through on the GPS data of the smartphone and then if 30 out of 40 people are stressed at the same intersection, it makes sense to change something there or investigate further what the potential drivers of stress are in this case\u201d<\/strong>.<\/strong><\/p>\nIT:U\u2019s Martin Moser, PhD.<\/cite><\/blockquote>\n\n\n\n
Martin Moser, PhD, with his supervisor, Professor Bernd Resch, and others at his PhD defense. <\/figcaption><\/figure>\n\n\n\n\n Method increasing perceived safety\u00a0<\/h3>\n\n\n\n
After being the first to defend his PhD at IT:U, Martin Moser says he is \u201crelieved and happy to see so many people attending, and proud to be IT:U\u2019s first graduate and to have completed my PhD journey\u201d.<\/strong>\u00a0<\/p>\n\n\n\n
His supervisor, Professor of Geosocial Artificial Intelligence, Bernd Resch, says it is a milestone. <\/p>\n\n\n\n
The research team\u2019s approach already garnered interest from authorities in Salzburg when they encountered a tricky traffic situation in the city\u2019s outskirts some time ago. In Faistenau, the construction of a new supermarket along a busy road created a potentially hazardous pathway. The analysis objectively highlighted the location and provided a solid basis for targeted measures to mitigate the danger.\u00a0<\/p>\n\n\n\n
\n\u201cOn one hand we drive a lot of research papers, but then again, what they did is go to the public and said: \u2018This is a scientifically sound algorithm and approach, and here we have it black and white, we need to build a bike path here\u2019.\u201d<\/strong><\/p>\nBernd Resch, <\/strong>Professor of Geosocial Artificial Intelligence at IT:U.<\/cite><\/blockquote>\n\n\n\n
Professor Resch says the algorithm is already working well, and if a city council or planning agency calls them with a specific problem at an intersection or a street, they can \u201cgo there, conduct a study, and help them sort out the planning\u201d.<\/strong>\u00a0<\/p>\n\n\n\n
But he cautions the method is still in its infancy, and there is stilla long way to go before it could be rolled out on a larger scale.\u00a0<\/p>\n\n\n\n
Martin Moser, PhD, and his team developed an algorithm that detects stress in cyclists and helps pinpoint danger areas. <\/figcaption><\/figure>\n\n\n\n\n IT:U\u2019s interdisciplinary approach helped shape research<\/strong> <\/h3>\n\n\n\n
Martin Moser started looking at the topic at the University of Salzburg<\/a>, and continued working on it at IT:U. Further longstanding partners include Karlsruher Institut f\u00fcr Technologie<\/a> (KIT), the city of Osnabr\u00fcck<\/a> and Outdooractive<\/a>.\u00a0<\/p>\n\n\n\n
He says the difference between the two universities is their focus on interdisciplinarity, with his research at IT:U requiring knowledge in various scholarly areas, including machine learning, urban planning, and explainable AI. <\/p>\n\n\n\n
His highlight these past years was writing his thesis, while he experienced his lowest point when an algorithm did not do its job as intended. <\/p>\n\n\n\n
But as he is a keen tennis player, his sportsman attitude helped him overcome the challenges and keep going. <\/p>\n\n\n\n
Martin Moser is staying at IT:U as a postdoc, where he plans to do further research in this field. <\/p>\n","protected":false},"excerpt":{"rendered":"
It is springtime, and people will often choose their bike over their car or public transport during the longer daylight hours when they feel safe on the streets. But what makes cyclists feel unsafe, and how can we measure it and ultimately improve their experience?\u00a0 Martin Moser, PhD, from IT:U\u2019s GeoSocial AI Research Group has […]<\/p>\n","protected":false},"author":18,"featured_media":18113,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"acf":[],"yoast_head":"\n
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